0. Low Rank Gaussian Process Regression- Fit a Low Rank Gaussian Process Regression / Linear Mixed Model for large datasets. These models are widely used in statistical genetics as a test of association while correcting for the confounding effects of kinship and population structure.

1. Bayesian Biclustering- This package uses a Bayesian spike-and-slab model to construct bidendrograms using log posterior as the natural distance defined by the model and calculates importance using log Bayes factor.

4. bclust- Bayesian clustering with variable selection is a fully automatic clustering method, designed for low-sample-size-high-dimensional situations. This might be regarded as a high-dimensional variant of the mclust package.

5. mixOmics- mixOmics is an R package that supplies different methodologies to unravel relationships between two heterogeneous data sets of size n × p and n × q where the p and q variables are measured on the same samples or individuals n.

8. SPRINT: Simple Parallel R INTerface- SPRINT is a Simple Parallel R INTerface to High Performance Computing (HPC) that enables biologists to run statistical analysis scripts using R in parallel on HPC clusters. It consists of an HPC harness and a library of parallelized R functions.

10. ParallABEL- ParallABEL aims to speed up the computation of GWA studies and also simplify parallelization in analysis of this important research area.
See http://www.sc.psu.ac.th/units/genome/CGBR/ParallABEL/index.html for more details.

12. survGenesInterim- Simulation of survival studies based on simulated gene expression level and patient data. Includes functions to generate such data. Resulting error and power rate can be visualized. Can be used for sample size or power estimation when planning a study.

15. casper- casper infers alternative splicing from high-throughput sequencing data both for known variants and de novo discovery. We use a Bayesian model with few assumptions, and modern model selection ideas with improved theoretical and computational properties.